public void CreateClustersMovieLens()
{
var reader = new MovieLensReader(Paths.MovieLens1M);
var dataset = new Dataset<ItemRating>(reader);
var clusterer = new Clusterer(dataset);
for (int i = 2; i < 15; i += 2)
{
clusterer.WriteUsersCluster(Paths.MovieLens1MUsersCluster + i + ".csv", i, 5);
clusterer.WriteItemsCluster(Paths.MovieLens1MItemsCluster + i + ".csv", i, 5);
}
}
public void CreateClustersMovieLens()
{
var reader = new MovieLensReader(Paths.MovieLens1M);
var dataset = new Dataset <ItemRating>(reader);
var clusterer = new Clusterer(dataset);
for (int i = 2; i < 15; i += 2)
{
clusterer.WriteUsersCluster(Paths.MovieLens1MUsersCluster + i + ".csv", i, 5);
clusterer.WriteItemsCluster(Paths.MovieLens1MItemsCluster + i + ".csv", i, 5);
}
}
public void TestMovieLensWithClusters()
{
// Todo: instead of loading the dataset every time add a updateCluster method to the Dataset<ItemRatingWithCluster>
List <string> rmses = new List <string>();
List <string> maes = new List <string>();
for (int i = 0; i < 15; i += 2)
{
string usersClusterFile = Paths.MovieLens1MUsersCluster + i + ".csv";
string itemsClusterFile = Paths.MovieLens1MItemsCluster + i + ".csv";
MovieLensReader trainReader, testReader;
if (i == 0)
{
trainReader = new MovieLensReader(Paths.MovieLens1MTrain75);
testReader = new MovieLensReader(Paths.MovieLens1MTest25);
}
else
{
trainReader = new MovieLensReader(Paths.MovieLens1MTrain75, usersClusterFile, itemsClusterFile);
testReader = new MovieLensReader(Paths.MovieLens1MTest25, usersClusterFile, itemsClusterFile);
}
var dataset = new Dataset <MovieLensItemRating>(trainReader, testReader);
var recommender = new LibFmTrainTester();
var context = new EvalutationContext <ItemRating>(recommender, dataset);
var ep = new EvaluationPipeline <ItemRating>(context);
ep.Evaluators.Add(new RMSE());
ep.Evaluators.Add(new MAE());
ep.Run();
rmses.Add(context["RMSE"].ToString());
maes.Add(context["MAE"].ToString());
}
Console.WriteLine("RMSEs--------------");
rmses.ForEach(Console.WriteLine);
Console.WriteLine("MAEs-------------");
maes.ForEach(Console.WriteLine);
}
public void TestMovieLensWithClusters()
{
// Todo: instead of loading the dataset every time add a updateCluster method to the Dataset<ItemRatingWithCluster>
List<string> rmses = new List<string>();
List<string> maes = new List<string>();
for (int i = 0; i < 15; i += 2)
{
string usersClusterFile = Paths.MovieLens1MUsersCluster + i + ".csv";
string itemsClusterFile = Paths.MovieLens1MItemsCluster + i + ".csv";
MovieLensReader trainReader, testReader;
if (i == 0)
{
trainReader = new MovieLensReader(Paths.MovieLens1MTrain75);
testReader = new MovieLensReader(Paths.MovieLens1MTest25);
}
else
{
trainReader = new MovieLensReader(Paths.MovieLens1MTrain75, usersClusterFile, itemsClusterFile);
testReader = new MovieLensReader(Paths.MovieLens1MTest25, usersClusterFile, itemsClusterFile);
}
var dataset = new Dataset<MovieLensItemRating>(trainReader, testReader);
var recommender = new LibFmTrainTester();
var context = new EvalutationContext<ItemRating>(recommender, dataset);
var ep = new EvaluationPipeline<ItemRating>(context);
ep.Evaluators.Add(new RMSE());
ep.Evaluators.Add(new MAE());
ep.Run();
rmses.Add(context["RMSE"].ToString());
maes.Add(context["MAE"].ToString());
}
Console.WriteLine("RMSEs--------------");
rmses.ForEach(Console.WriteLine);
Console.WriteLine("MAEs-------------");
maes.ForEach(Console.WriteLine);
}
